Sentence examples for statistical enhancement from inspiring English sources

Furthermore, when comparing goodness-of-fit factors (i.e. AIC, SC, and MSC), no statistical enhancement (or superior fitting) could be detected by one of the models (i.e. 2C1i and 3C2i).

Instead we propose that that the observed pattern of subcortical on-line plasticity results from the statistical enhancement of intrinsic circuitry interacting with top-down influences such as auditory memory, musical knowledge, expectation and/or grouping via the corticofugal pathway.

Thus, it appears that the brainstem, likely as a consequence of the statistical enhancement of intrinsic circuitry and corticofugal influence, locks onto temporal patterns occurring on multiple time scales [53], such as a local repetition within a recurring melody.

It is also possible that there was only a selection for, or statistical enhancement of, spontaneous mutations rather than de novo mutation induction.

Results from assays with both probes reflected only statistical enhancements, suggesting improper ligand spacing on the squalene scaffold for the divalent constructs.

At varying stages in the "Character match" mode, characters have a chance to be upgraded into "Star characters", which grants statistical enhancements.

The strong non-neural source of intra- and intersubject variability of BOLD response during the block design compared to event-related task indicates that study designs optimizing for statistical power through enhancement of the BOLD contrast (for, e.g., block design) can be affected by enhancement of non-neural sources of BOLD variability.

Considering MRI, we found a statistical association between mass enhancement and papilloma (P < 0.001; AUC 0.957; CI 0.888 1.025), ductal enhancement and papillomatosis (P < 0.001; AUC 0.790; CI 0.623 0.958), segmental enhancement and ductal cancer in situ (P = 0.007; AUC 0.750; CI 0.429 1.071), and linear enhancement and papillary cancer (P = 0.011).

According to the conclusions, representative frames of speech signals containing T, RDFT, IDFT, or DCT features that are often used in statistical model-based speech enhancement algorithms [9, 10, 28] can be better statistically modeled by the MLD than MGD distribution.

A priori SNR estimation and noise power estimation are key factors in statistical model-based speech enhancement.

A statistical analysis proves an enhancement in detection efficiency of more than 15% in a confocal and in a wide-field configuration.